{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/3"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/2"}],"enrichment":{"capability":"MNE-Python provides tools for exploring, visualizing, and analyzing human neurophysiological data including MEG, EEG, sEEG, and ECoG recordings, with modules for preprocessing, source estimation, time-frequency analysis, connectivity, and machine learning.","skillfed_tags":["neuroscience","signal-processing","brain-imaging"],"use_cases":["Load and preprocess raw MEG or EEG recordings, apply filters, and detect artifacts before analysis.","Estimate the location of neural sources in the brain from surface recordings using inverse methods.","Compute spectrograms, power spectral density, or cross-frequency coupling in neurophysiological data.","Analyze functional connectivity between brain regions using correlation or coherence measures.","Prepare neural data for machine learning pipelines for classification or regression on brain signals.","Visualize brain activity on cortical surfaces or as time-series plots for exploratory analysis."],"what_it_does":"MNE-Python is a mature, open-source toolkit for working with human brain recordings. It handles data from multiple modalities\u2014MEG, EEG, sEEG, ECoG, fNIRS\u2014and provides a complete pipeline from raw data import and preprocessing to visualization and statistical analysis. The package bundles modules for source localization, spectral and time-frequency decomposition, functional connectivity, and machine learning on neural data.\n\nThe package is built on standard scientific Python dependencies including numpy, scipy, and matplotlib, and includes convenience functions for common neuroimaging workflows. It is widely used in neuroscience research and clinical settings. Installation is straightforward via pip, and the codebase is actively maintained with comprehensive documentation and a user forum for support.","worth_installing":"Yes. MNE-Python is a well-established, actively maintained toolkit with no known vulnerabilities, permissive BSD-3-Clause licensing, and low install friction. It is the standard choice for neurophysiological data analysis in Python. Install it if you work with MEG, EEG, or related brain recordings."},"id":"mne","links":{"html":"https://skillfed.io/packages/mne","md":"https://skillfed.io/packages/mne.md","pypi":"https://pypi.org/project/mne/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-20","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"mne","python_support":"supports_current","summary":"MNE-Python project for MEG and EEG data analysis."},"popularity":{"monthly_downloads":4018846,"position":2397,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.12.1"}